Recommender systems have been developed to assist decision making by recommending a list of items to the end users. The multi-criteria recommender system (MCRS) is a special type of recommender systems, where user preferences on multiple criteria can be taken into account in recommendation models. Traditional algorithms for MCRS usually predict user ratings on these criteria, and finally estimate the overall rating by different aggregation functions. In this paper, we propose a novel multi-criteria recommendation framework, Multi-Criteria Ranking, where we can directly infer a ranking score for an item candidate from the predicted ratings on multiple criteria. The proposed framework is general enough and most of the existing algorithms in MCRS can be easily integrated with our framework. Our experimental results can demonstrate the effectiveness of the proposed framework by evaluating top- $N$ recommendations over multiple real-world data sets. We believe that multi-criteria ranking opens the door to develop more effective and promising multi-criteria recommendation models.